{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9be010e3",
   "metadata": {},
   "source": [
    "# Benchmarking pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88056c0f",
   "metadata": {},
   "source": [
    "Biological activities can be estimated by coupling a statistical method with a prior knowledge network. However, it is not clear which method or network performs better. When perturbation experiments are available, one can benchmark these methods by evaluating how good are they at recovering perturbed regulators. In our original `decoupler` [publication](https://doi.org/10.1093/bioadv/vbac016), we observed that `wsum`, `mlm`, `ulm` and `consensus` where the top performer methods. \n",
    "\n",
    "In this notebook we showcase how to use `decoupler` for the benchmarking of methods and prior knowledge networks. The data used here consist of single-gene perturbation experiments from the [KnockTF2](http://www.licpathway.net/KnockTFv2/index.php) database, an extensive dataset of literature curated perturbation experiments. If you use these data for your resarch, please cite the original publication of the resource:\n",
    "\n",
    ">Chenchen Feng, Chao Song, Yuejuan Liu, Fengcui Qian, Yu Gao, Ziyu Ning, Qiuyu Wang, Yong Jiang, Yanyu Li, Meng Li, Jiaxin Chen, Jian Zhang, Chunquan Li, KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors, Nucleic Acids Research, Volume 48, Issue D1, 08 January 2020, Pages D93–D100, https://doi.org/10.1093/nar/gkz881\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "This tutorial assumes that you already know the basics of `decoupler`. Else, check out the [Usage](https://decoupler-py.readthedocs.io/en/latest/notebooks/usage.html) tutorial first.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a2fe17a",
   "metadata": {},
   "source": [
    "## Loading packages\n",
    "\n",
    "First, we need to load the relevant packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c610d39",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import decoupler as dc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1552661f",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "\n",
    "We can download the data easily from Zenodo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7654e58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-01 11:09:30--  https://zenodo.org/record/7035528/files/knockTF_expr.csv?download=1\n",
      "Resolving zenodo.org (zenodo.org)... 188.185.124.72\n",
      "Connecting to zenodo.org (zenodo.org)|188.185.124.72|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 146086808 (139M) [text/plain]\n",
      "Saving to: ‘knockTF_expr.csv’\n",
      "\n",
      "knockTF_expr.csv    100%[===================>] 139,32M  18,8MB/s    in 47s     \n",
      "\n",
      "2023-06-01 11:10:17 (2,97 MB/s) - ‘knockTF_expr.csv’ saved [146086808/146086808]\n",
      "\n",
      "--2023-06-01 11:10:18--  https://zenodo.org/record/7035528/files/knockTF_meta.csv?download=1\n",
      "Resolving zenodo.org (zenodo.org)... 188.185.124.72\n",
      "Connecting to zenodo.org (zenodo.org)|188.185.124.72|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 144861 (141K) [text/plain]\n",
      "Saving to: ‘knockTF_meta.csv’\n",
      "\n",
      "knockTF_meta.csv    100%[===================>] 141,47K  --.-KB/s    in 0,03s   \n",
      "\n",
      "2023-06-01 11:10:18 (4,15 MB/s) - ‘knockTF_meta.csv’ saved [144861/144861]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://zenodo.org/record/7035528/files/knockTF_expr.csv?download=1' -O knockTF_expr.csv\n",
    "!wget 'https://zenodo.org/record/7035528/files/knockTF_meta.csv?download=1' -O knockTF_meta.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e11366",
   "metadata": {},
   "source": [
    "We can then read it using `pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25c778f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read data\n",
    "mat = pd.read_csv('knockTF_expr.csv', index_col=0)\n",
    "obs = pd.read_csv('knockTF_meta.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70634b2b",
   "metadata": {},
   "source": [
    "`mat` consist of a matrix of logFCs between the perturbed and the control samples. Positive values mean that these genes were\n",
    "overexpressed after the perturbation, negative values mean the opposite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fc3a459",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>-0.147140</td>\n",
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       "      <td>0.0000</td>\n",
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       "      <td>0.17104</td>\n",
       "      <td>-0.34852</td>\n",
       "      <td>-0.95517</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-0.286920</td>\n",
       "      <td>-0.01815</td>\n",
       "      <td>0.119410</td>\n",
       "      <td>0.077850</td>\n",
       "      <td>0.234740</td>\n",
       "      <td>0.228690</td>\n",
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       "      <td>0.141374</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>907 rows × 21985 columns</p>\n",
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      ],
      "text/plain": [
       "                    A1BG  A1BG-AS1      A1CF   A2LD1       A2M    A2ML1  \\\n",
       "DataSet_01_001  0.000000  0.000000  0.000000  0.0000  0.000000  0.00000   \n",
       "DataSet_01_002 -0.173750  0.000000 -0.537790 -0.4424 -1.596640 -0.00010   \n",
       "DataSet_01_003 -0.216130  0.000000 -0.220270 -0.0008  0.000000  0.15580   \n",
       "DataSet_01_004 -0.255680  0.111050 -0.285270  0.0000 -0.035860 -0.46970   \n",
       "DataSet_01_005  0.478500 -0.375710 -0.847180  0.0000  3.354450  0.17104   \n",
       "...                  ...       ...       ...     ...       ...      ...   \n",
       "DataSet_04_039  0.668721  0.000000  0.120893  0.0000  0.263564  0.00000   \n",
       "DataSet_04_040  1.816027  0.000000  0.139721  0.0000  0.459498  0.00000   \n",
       "DataSet_04_041 -0.174250  0.000000 -0.084499  0.0000  0.112492  0.00000   \n",
       "DataSet_04_042  0.976681  0.000000  0.099845  0.0000  0.366973  0.00000   \n",
       "DataSet_04_043  0.677944  0.584963  0.271215  0.0000  0.104090  0.00000   \n",
       "\n",
       "                 A4GALT    A4GNT  AA06  AAA1  ...     ZWINT     ZXDA  \\\n",
       "DataSet_01_001  0.00000  0.00000   0.0   0.0  ...  0.000000  0.00000   \n",
       "DataSet_01_002 -0.08260 -3.64590   0.0   0.0  ...  0.000000  0.39579   \n",
       "DataSet_01_003 -0.35802 -0.32025   0.0   0.0  ...  0.000000 -0.82964   \n",
       "DataSet_01_004  0.18559 -0.25601   0.0   0.0  ...  0.000000 -0.39888   \n",
       "DataSet_01_005 -0.34852 -0.95517   0.0   0.0  ...  0.000000  0.24849   \n",
       "...                 ...      ...   ...   ...  ...       ...      ...   \n",
       "DataSet_04_039  0.00000  0.00000   0.0   0.0  ... -0.324808  0.00000   \n",
       "DataSet_04_040  0.00000  0.00000   0.0   0.0  ... -0.321726  0.00000   \n",
       "DataSet_04_041  0.00000  0.00000   0.0   0.0  ...  0.405369  0.00000   \n",
       "DataSet_04_042  0.00000  0.00000   0.0   0.0  ...  0.397698  0.00000   \n",
       "DataSet_04_043  0.00000  0.00000   0.0   0.0  ... -0.357371  0.00000   \n",
       "\n",
       "                    ZXDB      ZXDC   ZYG11A    ZYG11B       ZYX     ZZEF1  \\\n",
       "DataSet_01_001  0.000000  0.000000  0.00000  0.000000 -0.033220 -0.046900   \n",
       "DataSet_01_002  0.000000  0.086110  0.28574 -0.130210 -0.276640 -0.881580   \n",
       "DataSet_01_003  0.000000  0.064470  0.59323  0.313950 -0.232600  0.065510   \n",
       "DataSet_01_004  0.000000  0.104440 -0.16434  0.191460  0.415610  0.393840   \n",
       "DataSet_01_005  0.000000 -0.286920 -0.01815  0.119410  0.077850  0.234740   \n",
       "...                  ...       ...      ...       ...       ...       ...   \n",
       "DataSet_04_039 -0.370491  0.092874  0.00000  0.014075 -0.215169  0.141374   \n",
       "DataSet_04_040 -0.144934 -0.238343  0.00000 -0.134995  0.577406  0.082028   \n",
       "DataSet_04_041  0.272526 -0.125475  0.00000  0.019763 -0.098584  0.072675   \n",
       "DataSet_04_042 -0.398350  0.037134  0.00000  0.409704 -0.056978  0.308122   \n",
       "DataSet_04_043 -0.152622 -0.076498  0.00000  0.047949 -0.169766  0.178053   \n",
       "\n",
       "                    ZZZ3  psiTPTE22  \n",
       "DataSet_01_001 -0.143830    0.00000  \n",
       "DataSet_01_002  0.812950    0.47896  \n",
       "DataSet_01_003 -0.147140    0.00000  \n",
       "DataSet_01_004  0.127900    0.00000  \n",
       "DataSet_01_005  0.228690    0.00000  \n",
       "...                  ...        ...  \n",
       "DataSet_04_039 -0.076054    0.00000  \n",
       "DataSet_04_040 -0.171424    0.00000  \n",
       "DataSet_04_041  0.199813    0.00000  \n",
       "DataSet_04_042 -0.058794    0.00000  \n",
       "DataSet_04_043  0.199003    0.00000  \n",
       "\n",
       "[907 rows x 21985 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c420680b",
   "metadata": {},
   "source": [
    "On the other hand, `obs` contains the meta-data information of each perturbation experiment.\n",
    "Here one can find which TF was perturbed (`TF` column), and many other useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa5396dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TF</th>\n",
       "      <th>Species</th>\n",
       "      <th>Knock.Method</th>\n",
       "      <th>Biosample.Name</th>\n",
       "      <th>Profile.ID</th>\n",
       "      <th>Platform</th>\n",
       "      <th>TF.Class</th>\n",
       "      <th>TF.Superclass</th>\n",
       "      <th>Tissue.Type</th>\n",
       "      <th>Biosample.Type</th>\n",
       "      <th>Data.Source</th>\n",
       "      <th>Pubmed.ID</th>\n",
       "      <th>logFC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DataSet_01_001</th>\n",
       "      <td>ESR1</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>siRNA</td>\n",
       "      <td>MCF7</td>\n",
       "      <td>GSE10061</td>\n",
       "      <td>GPL3921</td>\n",
       "      <td>Nuclear receptors with C4 zinc fingers</td>\n",
       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
       "      <td>Mammary_gland</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>GEO</td>\n",
       "      <td>18631401</td>\n",
       "      <td>-0.713850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_01_002</th>\n",
       "      <td>HNF1A</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>shRNA</td>\n",
       "      <td>HuH7</td>\n",
       "      <td>GSE103128</td>\n",
       "      <td>GPL18180</td>\n",
       "      <td>Homeo domain factors</td>\n",
       "      <td>Helix-turn-helix domains</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>GEO</td>\n",
       "      <td>29466992</td>\n",
       "      <td>0.164280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_01_003</th>\n",
       "      <td>MLXIP</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>shRNA</td>\n",
       "      <td>HA1ER</td>\n",
       "      <td>GSE11242</td>\n",
       "      <td>GPL4133</td>\n",
       "      <td>Basic helix-loop-helix factors (bHLH)</td>\n",
       "      <td>Basic domains</td>\n",
       "      <td>Embryo_kidney</td>\n",
       "      <td>Stem cell</td>\n",
       "      <td>GEO</td>\n",
       "      <td>18458340</td>\n",
       "      <td>0.262150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_01_004</th>\n",
       "      <td>CREB1</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>shRNA</td>\n",
       "      <td>K562</td>\n",
       "      <td>GSE12056</td>\n",
       "      <td>GPL570</td>\n",
       "      <td>Basic leucine zipper factors (bZIP)</td>\n",
       "      <td>Basic domains</td>\n",
       "      <td>Haematopoietic_and_lymphoid_tissue</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>GEO</td>\n",
       "      <td>18801183</td>\n",
       "      <td>-0.950180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_01_005</th>\n",
       "      <td>POU5F1</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>siRNA</td>\n",
       "      <td>GBS6</td>\n",
       "      <td>GSE12320</td>\n",
       "      <td>GPL570</td>\n",
       "      <td>Homeo domain factors</td>\n",
       "      <td>Helix-turn-helix domains</td>\n",
       "      <td>Bone_marrow</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>GEO</td>\n",
       "      <td>20203285</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_04_039</th>\n",
       "      <td>SRPK2</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>HepG2</td>\n",
       "      <td>ENCSR929EIP</td>\n",
       "      <td>-</td>\n",
       "      <td>Others</td>\n",
       "      <td>ENCODE_TF</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>ENCODE</td>\n",
       "      <td>22955616</td>\n",
       "      <td>-1.392551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_04_040</th>\n",
       "      <td>WRN</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>shRNA</td>\n",
       "      <td>HepG2</td>\n",
       "      <td>ENCSR093FHC</td>\n",
       "      <td>-</td>\n",
       "      <td>Others</td>\n",
       "      <td>ENCODE_TF</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>ENCODE</td>\n",
       "      <td>22955616</td>\n",
       "      <td>-0.173964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_04_041</th>\n",
       "      <td>YBX1</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>HepG2</td>\n",
       "      <td>ENCSR548OTL</td>\n",
       "      <td>-</td>\n",
       "      <td>Cold-shock domain factors</td>\n",
       "      <td>beta-Barrel DNA-binding domains</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>ENCODE</td>\n",
       "      <td>22955616</td>\n",
       "      <td>-2.025170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_04_042</th>\n",
       "      <td>ZC3H8</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>shRNA</td>\n",
       "      <td>HepG2</td>\n",
       "      <td>ENCSR184YDW</td>\n",
       "      <td>-</td>\n",
       "      <td>C3H zinc finger factors</td>\n",
       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>ENCODE</td>\n",
       "      <td>22955616</td>\n",
       "      <td>-0.027152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet_04_043</th>\n",
       "      <td>ZNF574</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>CRISPR</td>\n",
       "      <td>HepG2</td>\n",
       "      <td>ENCSR821GQN</td>\n",
       "      <td>-</td>\n",
       "      <td>C2H2 zinc finger factors</td>\n",
       "      <td>Zinc-coordinating DNA-binding domains</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Cell line</td>\n",
       "      <td>ENCODE</td>\n",
       "      <td>22955616</td>\n",
       "      <td>0.990298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>907 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    TF       Species Knock.Method Biosample.Name   Profile.ID  \\\n",
       "DataSet_01_001    ESR1  Homo sapiens        siRNA           MCF7     GSE10061   \n",
       "DataSet_01_002   HNF1A  Homo sapiens        shRNA           HuH7    GSE103128   \n",
       "DataSet_01_003   MLXIP  Homo sapiens        shRNA          HA1ER     GSE11242   \n",
       "DataSet_01_004   CREB1  Homo sapiens        shRNA           K562     GSE12056   \n",
       "DataSet_01_005  POU5F1  Homo sapiens        siRNA           GBS6     GSE12320   \n",
       "...                ...           ...          ...            ...          ...   \n",
       "DataSet_04_039   SRPK2  Homo sapiens       CRISPR          HepG2  ENCSR929EIP   \n",
       "DataSet_04_040     WRN  Homo sapiens        shRNA          HepG2  ENCSR093FHC   \n",
       "DataSet_04_041    YBX1  Homo sapiens       CRISPR          HepG2  ENCSR548OTL   \n",
       "DataSet_04_042   ZC3H8  Homo sapiens        shRNA          HepG2  ENCSR184YDW   \n",
       "DataSet_04_043  ZNF574  Homo sapiens       CRISPR          HepG2  ENCSR821GQN   \n",
       "\n",
       "                Platform                                TF.Class  \\\n",
       "DataSet_01_001   GPL3921  Nuclear receptors with C4 zinc fingers   \n",
       "DataSet_01_002  GPL18180                    Homeo domain factors   \n",
       "DataSet_01_003   GPL4133   Basic helix-loop-helix factors (bHLH)   \n",
       "DataSet_01_004    GPL570     Basic leucine zipper factors (bZIP)   \n",
       "DataSet_01_005    GPL570                    Homeo domain factors   \n",
       "...                  ...                                     ...   \n",
       "DataSet_04_039         -                                  Others   \n",
       "DataSet_04_040         -                                  Others   \n",
       "DataSet_04_041         -               Cold-shock domain factors   \n",
       "DataSet_04_042         -                 C3H zinc finger factors   \n",
       "DataSet_04_043         -                C2H2 zinc finger factors   \n",
       "\n",
       "                                        TF.Superclass  \\\n",
       "DataSet_01_001  Zinc-coordinating DNA-binding domains   \n",
       "DataSet_01_002               Helix-turn-helix domains   \n",
       "DataSet_01_003                          Basic domains   \n",
       "DataSet_01_004                          Basic domains   \n",
       "DataSet_01_005               Helix-turn-helix domains   \n",
       "...                                               ...   \n",
       "DataSet_04_039                              ENCODE_TF   \n",
       "DataSet_04_040                              ENCODE_TF   \n",
       "DataSet_04_041        beta-Barrel DNA-binding domains   \n",
       "DataSet_04_042  Zinc-coordinating DNA-binding domains   \n",
       "DataSet_04_043  Zinc-coordinating DNA-binding domains   \n",
       "\n",
       "                                       Tissue.Type Biosample.Type Data.Source  \\\n",
       "DataSet_01_001                       Mammary_gland      Cell line         GEO   \n",
       "DataSet_01_002                               Liver      Cell line         GEO   \n",
       "DataSet_01_003                       Embryo_kidney      Stem cell         GEO   \n",
       "DataSet_01_004  Haematopoietic_and_lymphoid_tissue      Cell line         GEO   \n",
       "DataSet_01_005                         Bone_marrow      Cell line         GEO   \n",
       "...                                            ...            ...         ...   \n",
       "DataSet_04_039                               Liver      Cell line      ENCODE   \n",
       "DataSet_04_040                               Liver      Cell line      ENCODE   \n",
       "DataSet_04_041                               Liver      Cell line      ENCODE   \n",
       "DataSet_04_042                               Liver      Cell line      ENCODE   \n",
       "DataSet_04_043                               Liver      Cell line      ENCODE   \n",
       "\n",
       "               Pubmed.ID     logFC  \n",
       "DataSet_01_001  18631401 -0.713850  \n",
       "DataSet_01_002  29466992  0.164280  \n",
       "DataSet_01_003  18458340  0.262150  \n",
       "DataSet_01_004  18801183 -0.950180  \n",
       "DataSet_01_005  20203285  0.000000  \n",
       "...                  ...       ...  \n",
       "DataSet_04_039  22955616 -1.392551  \n",
       "DataSet_04_040  22955616 -0.173964  \n",
       "DataSet_04_041  22955616 -2.025170  \n",
       "DataSet_04_042  22955616 -0.027152  \n",
       "DataSet_04_043  22955616  0.990298  \n",
       "\n",
       "[907 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d02df0",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "\n",
    "Since some of the experiments collected inside KnockTF are of low quality, it is best to remove them. We can do this by filtering\n",
    "perturbation experiments where the knocked TF does not show a strong negative logFC (meaning that the knock method did not work).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5450177",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((388, 21985), (388, 13))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out low quality experiments (positive logFCs in knockout experiments)\n",
    "msk = obs['logFC'] < -1\n",
    "mat = mat[msk]\n",
    "obs = obs[msk]\n",
    "mat.shape, obs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af388fa0",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "### Single network\n",
    "\n",
    "As an example, we will evaluate the gene regulatory network\n",
    "[CollecTRI](https://github.com/saezlab/CollecTRI), for more information you can visit this\n",
    "[other vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/dorothea.html).\n",
    "\n",
    "We can retrieve this network by running:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e66d6631-575b-4f1e-ae33-89e0f78e2d1d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYC</td>\n",
       "      <td>TERT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPI1</td>\n",
       "      <td>BGLAP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SMAD3</td>\n",
       "      <td>JUN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SMAD4</td>\n",
       "      <td>JUN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STAT5A</td>\n",
       "      <td>IL2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43173</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-143-3p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43174</th>\n",
       "      <td>AP1</td>\n",
       "      <td>hsa-miR-206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43175</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-21-5p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43176</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-224-5p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43177</th>\n",
       "      <td>AP1</td>\n",
       "      <td>hsa-miR-144</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43178 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       source          target  weight\n",
       "0         MYC            TERT       1\n",
       "1        SPI1           BGLAP       1\n",
       "2       SMAD3             JUN       1\n",
       "3       SMAD4             JUN       1\n",
       "4      STAT5A             IL2       1\n",
       "...       ...             ...     ...\n",
       "43173    NFKB  hsa-miR-143-3p       1\n",
       "43174     AP1     hsa-miR-206       1\n",
       "43175    NFKB   hsa-miR-21-5p       1\n",
       "43176    NFKB  hsa-miR-224-5p       1\n",
       "43177     AP1     hsa-miR-144       1\n",
       "\n",
       "[43178 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get collectri\n",
    "collectri = dc.get_collectri(organism='human', split_complexes=False)\n",
    "collectri"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c906e41",
   "metadata": {},
   "source": [
    "We will run the benchmark pipeline using the top performer methods from `decoupler`. If we would like, we could use any other,\n",
    "and we can optionaly change their parametters too (for more information check the `decouple` function).\n",
    "\n",
    "To run the benchmark pipeline, we need input molecular data (`mat`), its associated metadata (`obs`) with the name of the\n",
    "column where the perturbed regulators are (`perturb`), and which direction the pertrubations are (`sign`, positive or negative)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "737759f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting inputs...\n",
      "Formating net...\n",
      "109 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running wsum on mat with 279 samples and 21933 targets for 766 sources.\n",
      "Infering activities on 1 batches.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:17<00:00, 17.22s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 279 samples and 21933 targets for 766 sources.\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 279 samples and 21933 targets for 766 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Example on how to set up decouple arguments\n",
    "decouple_kws={\n",
    "    'methods' : ['wsum', 'ulm', 'mlm'],\n",
    "    'consensus': True,\n",
    "    'args' : {\n",
    "        'wsum' : {'times': 100}\n",
    "    }\n",
    "}\n",
    "\n",
    "# Run benchmark pipeline\n",
    "df = dc.benchmark(mat, obs, collectri, perturb='TF', sign=-1, verbose=True, decouple_kws=decouple_kws)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0c141a",
   "metadata": {},
   "source": [
    "The result of running the pipeline is a long-format dataframe containing different performance metrics across methods:\n",
    "\n",
    "- `auroc`: Area Under the Receiver Operating characteristic Curve (AUROC)\n",
    "- `auprc`: Area Under the Precision-Recall Curve (AUPRC), which by default uses a calibrated version where 0.5 indicates a random classifier (adapted from [this publication](https://doi.org/10.1007/978-3-030-44584-3_36)).\n",
    "- `mauroc`: Monte-Carlo Area Under the Precision-Recall Curve (AUPRC)\n",
    "- `mauprc`: Monte-Carlo Area Under the Receiver Operating characteristic Curve (AUROC)\n",
    "\n",
    "The Monte-Carlo metrics perform random permutations where each one samples the same number of true positives and true negatives to have balanced sets, returning one value per iteration done (10k by default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c98ebbd-db48-47e2-8cb2-1721692ccbeb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>groupby</th>\n",
       "      <th>group</th>\n",
       "      <th>source</th>\n",
       "      <th>method</th>\n",
       "      <th>metric</th>\n",
       "      <th>score</th>\n",
       "      <th>ci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>wsum_estimate</td>\n",
       "      <td>auroc</td>\n",
       "      <td>0.709890</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>wsum_estimate</td>\n",
       "      <td>auprc</td>\n",
       "      <td>0.735267</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>wsum_estimate</td>\n",
       "      <td>mcauroc</td>\n",
       "      <td>0.718978</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>wsum_estimate</td>\n",
       "      <td>mcauroc</td>\n",
       "      <td>0.730129</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>wsum_estimate</td>\n",
       "      <td>mcauroc</td>\n",
       "      <td>0.715317</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12007</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>consensus_estimate</td>\n",
       "      <td>mcauprc</td>\n",
       "      <td>0.771847</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12008</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>consensus_estimate</td>\n",
       "      <td>mcauprc</td>\n",
       "      <td>0.791121</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12009</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>consensus_estimate</td>\n",
       "      <td>mcauprc</td>\n",
       "      <td>0.768232</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12010</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>consensus_estimate</td>\n",
       "      <td>mcauprc</td>\n",
       "      <td>0.782516</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12011</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>consensus_estimate</td>\n",
       "      <td>mcauprc</td>\n",
       "      <td>0.783199</td>\n",
       "      <td>0.001305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12012 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      groupby group source              method   metric     score        ci\n",
       "0        None  None   None       wsum_estimate    auroc  0.709890  0.001305\n",
       "1        None  None   None       wsum_estimate    auprc  0.735267  0.001305\n",
       "2        None  None   None       wsum_estimate  mcauroc  0.718978  0.001305\n",
       "3        None  None   None       wsum_estimate  mcauroc  0.730129  0.001305\n",
       "4        None  None   None       wsum_estimate  mcauroc  0.715317  0.001305\n",
       "...       ...   ...    ...                 ...      ...       ...       ...\n",
       "12007    None  None   None  consensus_estimate  mcauprc  0.771847  0.001305\n",
       "12008    None  None   None  consensus_estimate  mcauprc  0.791121  0.001305\n",
       "12009    None  None   None  consensus_estimate  mcauprc  0.768232  0.001305\n",
       "12010    None  None   None  consensus_estimate  mcauprc  0.782516  0.001305\n",
       "12011    None  None   None  consensus_estimate  mcauprc  0.783199  0.001305\n",
       "\n",
       "[12012 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14098ba7",
   "metadata": {},
   "source": [
    "We can easily visualize the results in a scatterplot: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8ad87dd-892a-4787-9cf5-5acd6b8077fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter(df, x='mcauroc', y='mcauprc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c647ca",
   "metadata": {},
   "source": [
    "It looks like `mlm` this time had worse performance than other methods like `ulm` at recovering\n",
    "perturbed regulators, this might be caused because there are many TFs that share too many targets.\n",
    "Whenever there are multiple sources (TFs) and they share a lot of targets we recommend to use `ulm`.\n",
    "On the other hand, when there are few sources and they are orthogonal (they share few targets), we recommend to use `mlm`.\n",
    "Since `consensus` remains one of the top performer methods we also recommend its use but at a higher computational cost.\n",
    "\n",
    "We can also visualise the distirbutions of the obtained Monte-Carlo metrics, for example `mcauroc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4447de0f-d6bc-4fc2-8a56-a0a29f27d0c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_boxplot(df, metric='mcauroc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d7c52e",
   "metadata": {},
   "source": [
    "### Multiple networks\n",
    "\n",
    "This pipeline can evaluate multiple networks at the same time. Now we will evaluate CollecTRI and\n",
    "[DoRothEA](https://saezlab.github.io/dorothea/), a literature-based network that we developed in the past.\n",
    "We will also add a randomized version as control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a34abe69",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Get dorothea\n",
    "dorothea = dc.get_dorothea(organism='human')\n",
    "\n",
    "# Randomize nets\n",
    "ctri_rand = dc.shuffle_net(collectri, target='target', weight='weight').drop_duplicates(['source', 'target'])\n",
    "doro_rand = dc.shuffle_net(dorothea, target='target', weight='weight').drop_duplicates(['source', 'target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65089089",
   "metadata": {},
   "source": [
    "We can run the same pipeline but now using a dictionary of networks (and of extra arguments if needed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "029d1161",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using r_collectri network...\n",
      "Extracting inputs...\n",
      "Formating net...\n",
      "109 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 279 samples and 21933 targets for 762 sources.\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 279 samples and 21933 targets for 762 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.62s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n",
      "Using r_dorothea network...\n",
      "Extracting inputs...\n",
      "Formating net...\n",
      "175 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "55 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 213 samples and 21930 targets for 295 sources.\n",
      "55 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 213 samples and 21930 targets for 295 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n",
      "Using collectri network...\n",
      "Extracting inputs...\n",
      "Formating net...\n",
      "109 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 279 samples and 21933 targets for 766 sources.\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 279 samples and 21933 targets for 766 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.49s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n",
      "Using dorothea network...\n",
      "Extracting inputs...\n",
      "Formating net...\n",
      "174 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "55 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 214 samples and 21930 targets for 297 sources.\n",
      "55 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 214 samples and 21930 targets for 297 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Build dictionary of networks to test\n",
    "nets = {\n",
    "    'r_collectri': ctri_rand,\n",
    "    'r_dorothea': doro_rand,\n",
    "    'collectri': collectri,\n",
    "    'dorothea': dorothea,\n",
    "}\n",
    "\n",
    "# Example extra arguments\n",
    "decouple_kws = {\n",
    "    'r_collectri': {\n",
    "        'methods' : ['ulm', 'mlm'],\n",
    "        'consensus': True\n",
    "    },\n",
    "    'r_dorothea': {\n",
    "        'methods' : ['ulm', 'mlm'],\n",
    "        'consensus': True\n",
    "    },\n",
    "    'collectri': {\n",
    "        'methods' : ['ulm', 'mlm'],\n",
    "        'consensus': True\n",
    "    },\n",
    "    'dorothea': {\n",
    "        'methods' : ['ulm', 'mlm'],\n",
    "        'consensus': True\n",
    "    }\n",
    "}\n",
    "\n",
    "# Run benchmark pipeline\n",
    "df = dc.benchmark(mat, obs, nets, perturb='TF', sign=-1, verbose=True, decouple_kws=decouple_kws)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c37c44e",
   "metadata": {},
   "source": [
    "Like before we can try to visualize the results in a scatterplot, this time grouping by the network used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adb64864-b4b8-49a3-8f78-d170cf6ae050",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter(df, groupby='net', x='mcauroc', y='mcauprc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d852c7",
   "metadata": {},
   "source": [
    "Since this plot is too crowded, we can plot it again separating the methods by columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a446ffd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter_cols(df, col='method', figsize=(9, 5), groupby='net')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a71056c",
   "metadata": {},
   "source": [
    "Or by selecting only one of the methods, in this case `consensus` since it is the most reliable method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1851c3e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter(df[df['method'] == 'consensus_estimate'], groupby='net', x='mcauroc', y='mcauprc', show_text=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16dc3380",
   "metadata": {},
   "source": [
    "As expected, the random network has a performance close to 0.5 while the rest show higher predictive performance. As previously reported, the collectri network has better performance than dorothea.\n",
    "\n",
    "If needed, we can also plot the distributions of the Monte-Carlo metrics, now grouped by network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36aedb27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_boxplot(df, metric='mcauroc', groupby='net')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ccaa001",
   "metadata": {},
   "source": [
    "### By source\n",
    "\n",
    "The benchmark pipeline also allows to test the performance of individual regulators using the argument `by='source'`. For simplicity let us just use the best performing network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "389c20fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting inputs...\n",
      "Formating net...\n",
      "109 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 279 samples and 21933 targets for 766 sources.\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 279 samples and 21933 targets for 766 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.55s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Example on how to set up decouple arguments\n",
    "decouple_kws={\n",
    "    'methods' : ['ulm', 'mlm'],\n",
    "    'consensus': True,\n",
    "}\n",
    "\n",
    "# Run benchmark pipeline\n",
    "df = dc.benchmark(mat, obs, collectri, perturb='TF', sign=-1, by='source', verbose=True, decouple_kws=decouple_kws)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a76e8b",
   "metadata": {},
   "source": [
    "We can plot the results with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3ff2d43",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter_cols(df, col='source', figsize=(9, 5), groupby='method')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc4336d",
   "metadata": {},
   "source": [
    "It looks like we get good predictions for HIF1A, RELA and SOX2 among others. On the other side, we worse predictions for STAT1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3e1bbe",
   "metadata": {},
   "source": [
    "### By meta-data groups\n",
    "\n",
    "The benchmarking pipeline can also be run by grouping the input experiments using the `groupby` argument. Multiple groups can be selected at the same time but for simplicity, we will just test which knockout method seems to perform the best inhibitions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ad8a428",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting inputs...\n",
      "Formating net...\n",
      "109 experiments without sources in net, they will be removed.\n",
      "Running methods...\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running ulm on mat with 279 samples and 21933 targets for 766 sources.\n",
      "52 features of mat are empty, they will be removed.\n",
      "Running mlm on mat with 279 samples and 21933 targets for 766 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.39s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating metrics...\n",
      "Computing metrics for groupby Knock.Method...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# Example on how to set up decouple arguments\n",
    "decouple_kws={\n",
    "    'methods' : ['ulm', 'mlm'],\n",
    "    'consensus': True,\n",
    "}\n",
    "\n",
    "# Run benchmark pipeline\n",
    "df = dc.benchmark(mat, obs, collectri, perturb='TF', sign=-1, groupby='Knock.Method', verbose=True, decouple_kws=decouple_kws)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82448007",
   "metadata": {},
   "source": [
    "Again, we can visualize the results in a collection of scatterplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "80030b93",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_metrics_scatter_cols(df, col='method', figsize=(9, 5), groupby='group')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2f04d5",
   "metadata": {},
   "source": [
    "From our limited data-set, we can see that apparently shRNA works better than other knockout protocols."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dcp39",
   "language": "python",
   "name": "dcp39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
